3
$\begingroup$

There's some discussion on what F-measure means. I understand that the beta parameter determines the weight of recall in the combined score. In specific one answer states that "for good models using the $F_{\beta}$ implies you consider false negatives $\beta^2$ times more costly than false positives." beta < 1 lends more weight to precision, while beta > 1 favors recall (beta -> 0 considers only precision, beta -> +inf only recall).

If you want to weight precision or recall higher than the other, how do you decide on the beta? I'm a bit unclear on the math behind the F-measure, so does a beta = .5 mean that precision is weighted 2x as much as recall?

$\endgroup$
1
  • 1
    $\begingroup$ From $\beta^2$, $\beta=0.5$ would suggest that precision would be weighted 4 times as much as recall, at least according to the one answer cited. $\endgroup$
    – Carl
    Feb 29 '20 at 2:14
3
$\begingroup$

Don't use F scores at all. Every criticism of accuracy collected at Why is accuracy not the best measure for assessing classification models? applies completely equally to precision, recall and all F scores. Instead, use proper scoring rules.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.